ArticleSensors (Basel, Switzerland)2020
Subtask Segmentation of Timed Up and Go Test for Mobility Assessment of Perioperative Total Knee Arthroplasty.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07367789 (Prediction of Patient-reported Outcome Measure and Performance-based Measure After Total Knee Arthroplasty Using Instrumented Insoles and Deep Neural Networks), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Prediction of Patient-reported Outcome Measure and Performance-based Measure After Total Knee Arthroplasty Using Instrumented Insoles and Deep Neural Networks
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- The efficacy and tolerability of proteoglycan F in the treatment of knee osteoarthritis: A prospective, randomized, double-blind controlled trial.Osteoarthritis and cartilage open · 2024Trial
- Efficacy of interspace between the popliteal artery and the capsule of the posterior knee (iPACK) block versus periarticular local infiltration analgesia after unilateral total knee arthroplasty: Prospective randomized control trial.Saudi medical journal · 2021Trial
- Agreement between markerless and marker-based motion capture for knee kinematics and kinetics during functional activities in knee osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Validation of a Virtual Reality-Based Timed Up-and-Go Test Using Body-Worn Motion Trackers.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation-a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Tug-vimu: a GoPro-based mobility assessment bridging the gap between technology and clinical practice.Journal of neuroengineering and rehabilitation · 2025Article
- Inertial Measurement Unit-Based Frozen Shoulder Identification from Daily Shoulder Tasks Using Machine Learning Approaches.Sensors (Basel, Switzerland) · 2024Article
- L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Detection of kinematic abnormalities in persons with knee osteoarthritis using markerless motion capture during functional movement screen and daily activities.Frontiers in bioengineering and biotechnology · 2024Article
- Study protocol for a randomized controlled trial: evaluating the effect of isokinetic eccentric training of the hamstring on knee function and walking function after total knee arthroplasty.Frontiers in medicine · 2024Article
- Role of Muscle Ultrasound for the Study of Frailty in Elderly Patients with Diabetes: A Pilot Study.Biology · 2023Article
- Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.Sensors (Basel, Switzerland) · 2022Article
- Inertial Measurement Units and Application for Remote Health Care in Hip and Knee Osteoarthritis: Narrative Review.JMIR rehabilitation and assistive technologies · 2022Review
- Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research : a call to emphasize data quality and indications.Bone & joint open · 2022Article
- Quantification of the Link between Timed Up-and-Go Test Subtasks and Contractile Muscle Properties.Sensors (Basel, Switzerland) · 2021Article
- Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Total knee arthroplasty (TKA) is one of the most common treatments for people with severe knee osteoarthritis (OA). The accuracy of outcome measurements and quantitative assessments for perioperative TKA is an important issue in clinical practice. Timed up and go (TUG) tests have been validated to measure basic mobility and balance capabilities. A TUG test contains a series of subtasks, including sit-to-stand, walking-out, turning, walking-in, turning around, and stand-to-sit tasks. Detailed information about subtasks is essential to aid clinical professionals and physiotherapists in making assessment decisions. The main objective of this study is to design and develop a subtask segmentation approach using machine-learning models and knowledge-based postprocessing during the TUG test for perioperative TKA. The experiment recruited 26 patients with severe knee OA (11 patients with bilateral TKA planned and 15 patients with unilateral TKA planned). A series of signal-processing mechanisms and pattern recognition approaches involving machine learning-based multi-classifiers, fragmentation modification and subtask inference are designed and developed to tackle technical challenges in typical classification algorithms, including motion variability, fragmentation and ambiguity. The experimental results reveal that the accuracy of the proposed subtask segmentation approach using the AdaBoost technique with a window size of 128 samples is 92%, which is an improvement of at least 15% compared to that of the typical subtask segmentation approach using machine-learning models only.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.